Data Acquisition Databases
Acronyms: OLTP - On Line Transaction Processing
Examples:
- Order entry system;
- Look up your checking account when you go to an ATM to request a
withdraw
Features:
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Designed for very rapid selects and inserts of simple transactions
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Simple transaction that needs to be executed with speed.
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DBMS designed for OLTP (Oracle) do not do the best job at data
querying.
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Several databases are designed to query and manage data
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Stores transactional data of an enterprise
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A database is nothing more, or less, than a technology for managing
data files.
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OLTP Transactional data focusing particular operations or department
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Current data only, no historical data
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For OLTP systems that means small tables (row size), probably some
specific indexes related to transactional processing, and a high degree
of normalization
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A Database is normalized and contain several constraints to minimize
input errors.
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Contains only fine-grain data, no coarse grain aggregations
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The database is the underlying infrastructure of a data warehouse
(DW)
.
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The database is the underlying infrastructure of a data warehouse (DW).
Data Analysis Databases
Features:
designed for massive ad- hoc queries of large data volumes
not to process transactions.
Stores historical data of an enterprise
Datawarehouse is a centralized storage facility
Used for reporting purposes; helps management making critical decisions
For analysis of patterns, derived after analyzing data aggregations
datawarehouse does not contain all records/info, only summarized info
data gathered from a variety of sources and retained for extend periods
Integrated data formatted for easy access for queries and reports- trend
analysis
May contain all relevant detail transaction info for tracebility and drill down
of summaries.
There is need for good, clean, transactional data in the warehouse
The summaries and aggregations are also in
Larger tables, more indexes to facilitate queries, and many tables are
denormalized to varying degrees
Implemented using a database engine, RDBMS or OLAP tools
The schema is not normalized as in operational database.
The data are arranged in dimensions like Time, Geographic Region,
Product, Customer class, promotion etc.
The user doesn't need to know SQL or other language to access the
database.
A data warehouse does not normally accept user inputs and is read only.
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Contains fine-grain as well as coarse grain aggregate data
Summaries inside the relational warehouse could be a simple star schema
If you use a microstrategy to provide information, you will need a snow flake
schema.
If you use a hyperion solution, you must have this summarized area in star
schema.
Besides this site for feedback from others, I would recommend;
www.dwinfocenter.org. This site has a "getting started" link and recommends
some excellent books to start with. Also, www.dw-institute.com, The Data
Warehouse Institue is a great source for education.
www.dmreview.com is an excellent site for articles and you can get a free
subscription to their magazine
There are also a number of excellent resources available on the web, starting
right here at datawarehouse.com.
www.dmreview.com - hosted by DM Review magazine, and has a large archive
of articles on a broad range of topics, many related to data warehousing
www.datawarehousing.org
They key in using a datawarehouse in Oracle is to make sure that you use a star
schema and that you have the constraints, bmap indexes,init.ora file and
dimension objects set up correctly. This is very important in order for Oracle's
Cost Based Optimizer to work efficently.
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